refinire-rag-chroma 0.0.5

ChromaDB VectorStore plugin for refinire-rag

refinire-rag-chroma

ChromaDB VectorStore plugin for refinire-rag, providing seamless integration with ChromaDB for vector storage and similarity search.

Features

• ✅ Zero Configuration: Works out of the box with sensible defaults
• ✅ Environment Variable Configuration: Configure via REFINIRE_RAG_CHROMA_* environment variables
• ✅ Full refinire-rag v0.1.1+ Compatibility: Implements the complete VectorStore interface
• ✅ DocumentProcessor Integration: Supports refinire-rag processing pipelines
• ✅ Persistent and In-Memory Storage: Choose between persistent disk storage or in-memory
• ✅ Multiple Distance Metrics: Support for cosine, L2, and inner product distance
• ✅ Production Ready: Comprehensive error handling, logging, and validation

Quick Start

Zero Configuration Usage

from refinire_rag_chroma import ChromaVectorStore

# Works immediately with default settings
vector_store = ChromaVectorStore()

# Add documents (requires embedder)
vector_store.set_embedder(your_embedder)
processed_docs = list(vector_store.process(documents))

Environment Variable Configuration

# Set environment variables
export REFINIRE_RAG_CHROMA_COLLECTION_NAME="my_documents"
export REFINIRE_RAG_CHROMA_PERSIST_DIRECTORY="/data/chroma"
export REFINIRE_RAG_CHROMA_DISTANCE_METRIC="cosine"

from refinire_rag_chroma import ChromaVectorStore

# Automatically uses environment variables
vector_store = ChromaVectorStore()

Installation

pip install refinire-rag-chroma

Or with uv:

uv add refinire-rag-chroma

Configuration

Environment Variables

Variable	Default	Description
REFINIRE_RAG_CHROMA_COLLECTION_NAME	"refinire_documents"	Collection name
REFINIRE_RAG_CHROMA_PERSIST_DIRECTORY	None	Storage directory (None = in-memory)
REFINIRE_RAG_CHROMA_DISTANCE_METRIC	"cosine"	Distance metric ("cosine", "l2", "ip")
REFINIRE_RAG_CHROMA_BATCH_SIZE	100	Batch size for operations
REFINIRE_RAG_CHROMA_MAX_RETRIES	3	Maximum retry attempts
REFINIRE_RAG_CHROMA_AUTO_CREATE_COLLECTION	"true"	Auto-create collection
REFINIRE_RAG_CHROMA_AUTO_CLEAR_ON_INIT	"false"	Clear on initialization

Parameter-based Configuration

from refinire_rag_chroma import ChromaVectorStore

# Override specific settings with parameters
vector_store = ChromaVectorStore(
    collection_name="custom_collection",
    persist_directory="/path/to/storage",
    distance_metric="l2"
)

Usage Examples

Basic Vector Operations

import numpy as np
from refinire_rag_chroma import ChromaVectorStore
from refinire_rag.storage import VectorEntry

vector_store = ChromaVectorStore()

# Add a vector
entry = VectorEntry(
    document_id="doc1",
    content="Sample document",
    embedding=np.array([0.1, 0.2, 0.3, 0.4, 0.5]),
    metadata={"source": "example"}
)
vector_store.add_vector(entry)

# Search similar vectors
query_vector = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
results = vector_store.search_similar(query_vector, limit=10)

Document Processing Pipeline

from refinire_rag_chroma import ChromaVectorStore
from refinire_rag.models.document import Document

# Set up vector store with embedder
vector_store = ChromaVectorStore()
vector_store.set_embedder(your_embedder)

# Process documents
documents = [
    Document(id="1", content="First document", metadata={}),
    Document(id="2", content="Second document", metadata={})
]

# Documents are automatically embedded and stored
processed_docs = list(vector_store.process(documents))

Metadata Filtering

# Search by metadata
results = vector_store.search_by_metadata(
    filters={"source": "wikipedia"},
    limit=50
)

# Count vectors with filters
count = vector_store.count_vectors(filters={"category": "science"})

Docker Usage

FROM python:3.10

ENV REFINIRE_RAG_CHROMA_COLLECTION_NAME=production_docs
ENV REFINIRE_RAG_CHROMA_PERSIST_DIRECTORY=/data/chroma
ENV REFINIRE_RAG_CHROMA_DISTANCE_METRIC=cosine

COPY . /app
WORKDIR /app
RUN pip install refinire-rag-chroma

CMD ["python", "app.py"]

Docker Compose

version: '3.8'
services:
  app:
    image: my-app:latest
    environment:
      - REFINIRE_RAG_CHROMA_COLLECTION_NAME=production_docs
      - REFINIRE_RAG_CHROMA_PERSIST_DIRECTORY=/data/chroma
      - REFINIRE_RAG_CHROMA_BATCH_SIZE=200
    volumes:
      - chroma_data:/data/chroma

volumes:
  chroma_data:

API Reference

ChromaVectorStore

The main vector store implementation that supports both the VectorStore and DocumentProcessor interfaces.

Methods

• add_vector(entry: VectorEntry) -> str: Add a single vector
• add_vectors(entries: List[VectorEntry]) -> List[str]: Add multiple vectors
• get_vector(document_id: str) -> Optional[VectorEntry]: Retrieve a vector
• update_vector(entry: VectorEntry) -> bool: Update a vector
• delete_vector(document_id: str) -> bool: Delete a vector
• search_similar(query_vector: np.ndarray, limit: int, threshold: Optional[float], filters: Optional[Dict]) -> List[VectorSearchResult]: Search similar vectors
• search_by_metadata(filters: Dict, limit: int) -> List[VectorSearchResult]: Search by metadata only
• count_vectors(filters: Optional[Dict]) -> int: Count vectors
• get_stats() -> VectorStoreStats: Get store statistics
• clear() -> bool: Clear all vectors
• set_embedder(embedder: Any) -> None: Set embedder for processing
• process(documents: Iterable[Document], config: Optional[Any]) -> Iterator[Document]: Process documents

Configuration

ChromaVectorStore automatically reads configuration from environment variables with sensible defaults. You can override specific settings by passing parameters to the constructor.

Development

Setup

git clone https://github.com/your-repo/refinire-rag-chroma
cd refinire-rag-chroma
uv install

Testing

uv run pytest

With Coverage

uv run pytest --cov=src

Requirements

• Python 3.8+
• refinire-rag >=0.1.1
• chromadb >=0.4.0
• numpy

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run tests and ensure they pass
6. Submit a pull request

Changelog

v0.0.1

• Initial release
• Full refinire-rag v0.1.1+ compatibility
• Environment variable configuration system
• Zero-configuration deployment support
• DocumentProcessor integration
• Comprehensive test suite